比较与tf.expand_dims的差异

tf.expand_dims

tf.expand_dims(x, axis, name=None) -> Tensor

更多内容详见tf.expand_dims。

mindspore.ops.expand_dims

mindspore.ops.expand_dims(input_x, axis) -> Tensor

更多内容详见mindspore.ops.expand_dims。

差异对比

TensorFlow：对输入x在给定的轴上添加额外维度。

MindSpore：MindSpore此API实现功能与TensorFlow一致，仅参数名不同。

分类	子类	TensorFlow	MindSpore	差异
参数	参数1	x	input_x	功能一致，参数名不同
	参数2	axis	axis	-
	参数3	name	-	不涉及

代码示例1

两API实现功能一致，用法相同。

# TensorFlow
import numpy as np
import tensorflow as tf

x = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], dtype=np.float32)
axis = 1
out = tf.expand_dims (x, axis).numpy()
print(out)
# [[[ 1.  2.  3.  4.]]
#  [[ 5.  6.  7.  8.]]
#  [[ 9. 10. 11. 12.]]]

# MindSpore
import mindspore
import numpy as np
import mindspore.ops as ops
from mindspore import Tensor

input_params = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]), mindspore.float32)
axis = 1
output = ops.expand_dims(input_params,  axis)
print(output)
# [[[ 1.  2.  3.  4.]]
#  [[ 5.  6.  7.  8.]]
#  [[ 9. 10. 11. 12.]]]

代码示例2

两API实现功能一致，用法相同。

# TensorFlow
import numpy as np
import tensorflow as tf

x = np.array([[1,1,1]], dtype=np.float32)
axis = 2
out = tf.expand_dims (x, axis).numpy()
print(out)
# [[[1.]
#   [1.]
#   [1.]]]


# MindSpore
import mindspore
import numpy as np
import mindspore.ops as ops
from mindspore import Tensor

input_params = Tensor(np.array([[1,1,1]]), mindspore.float32)
axis = 2
output = ops.expand_dims(input_params,  axis)
print(output)
# [[[1.]
#   [1.]
#   [1.]]]